1. Field of the Invention
The present invention relates to an apparatus and a method for processing a radiographic image that is obtained by irradiating a subject with a radioactive ray (e.g., an X-ray).
2. Description of the Related Art
The diagnosing method, which includes irradiating a subject with an X-ray (i.e., one of various types of radioactive rays) and capturing an X-ray fluoroscopic image (i.e., an X-ray image) based on the X-ray having penetrated through the subject, is widely available in the medical field, so that acquired X-ray images can be used for various medical cares. To prevent a subject from being exposed to an excessive amount of X-ray, the dose of the X-ray that may be used for capturing an X-ray image is generally set to a very weak level. Therefore, the captured X-ray image tends to be an image including a large amount of random noise that depends on energy particles of the X-ray. Accordingly, to improve the visibility of an X-ray image, it is very important to perform noise suppression processing in an X-ray fluoroscopic imaging operation.
To enhance the effect of noise suppression, an accurate estimation of a noise amount included in an X-ray image is necessary. For example, an edge may be blurred if the degree of the applied noise suppression processing is excessive, or the noise may not be removed if the noise suppression processing is insufficient.
According to a conventional method for estimating the amount of noise included in an X-ray image, for example, as discussed in Japanese Patent No. 3762725, it may be useful to analyze a subject in each frame of a moving image and estimate a noise amount of each subject. In this case, the technique discussed in Japanese Patent No. 3762725 is described based on a video camera. However, similar effects may be obtained for X-ray images.
If a subject is thick, the X-ray cannot easily penetrate through the subject. In other words, a thick subject decreases the amount of an X-ray dose that may be detected by an X-ray sensor. Therefore, the X-ray image may include a relatively large amount of noise compared to an output value of an X-ray sensor signal (i.e., an X-ray image signal). Accordingly, it is effective to estimate the noise amount for each subject.
The random noise included in an X-ray image can be expressed as a variation amount of a pixel value in a specific region in a case where the irradiation X-ray dose has a constant intensity “X.” The random noises can be classified into two types of noises, i.e., a random quantum noise and an electric random system noise. The random quantum noise may change the pixel value with a standard deviation σq(X) resulting from the X-ray dose “X.” The electric random system noise may change the pixel value with a standard deviation σs that may be received from an X-ray sensor or a peripheral electric circuit.
It can be analyzed that the above-described two types of random noises are added as a random noise to an X-ray image. The following formula (1) defines an X-ray random noise amount σ(X), which is a standard deviation of the random noise.
When “X” represents an X-ray intensity, the variable X is considered as equivalent to an average pixel value of an X-ray image. In formula (1), σq(X) is dependent on the X-ray intensity “X” and variable according to the following formula (2). In formula (2), Kq is a conversion coefficient that can be used to calculate a noise amount from the X-ray intensity. In formula (1), σs is a constant value representing electric thermal noise, which is independent from the X-ray intensity.
                              σ          ⁡                      (            X            )                          =                                                                              σ                  q                                ⁡                                  (                  X                  )                                            2                        +                          σ              s              2                                                          (        1        )                                          σ          q                =                              K            q                    ·                                    (              X              )                                      1              2                                                          (        2        )            
FIG. 12 is a characteristic graph illustrating an example of a relationship between the X-ray intensity “X” and the X-ray random noise amount σ(X), which can be obtained according to formula (1). In FIG. 12, the abscissa axis represents the X-ray intensity “X” and the ordinate axis represents the X-ray random noise amount σ(X) that is a standard deviation of the random noise.
In FIG. 12, a straight line 1201 represents a relationship between the X-ray intensity “X” and the random quantum noise amount σq(X), and a straight line 1202 represents a relationship between the X-ray intensity “X” and the random system noise amount σs. In FIG. 12, a curve 1203 represents a random noise amount σ(X) that is a sum of the random quantum noise amount σq(X) and the random system noise amount σs.
As understood from the relationship illustrated in FIG. 12, the random system noise has larger effects in the X-ray intensity region (i.e., a low dose region) indicated by “A”, compared to the random quantum noise.
The X-ray image acquired from an X-ray sensor generally includes dark components. In this description, the term “dark” indicates a constant offset amount. A method for correcting the offset can be referred to as a dark correction method.
A basic dark correction method includes removing a “dark image” (i.e., an image captured without using an X-ray) from an X-ray image (i.e., an image captured when a subject is irradiated with an X-ray). There are various types of dark correction methods that are different in the method for selecting dark image(s). For example, the dark correction methods may include a “forward” dark correction, a “backward” dark correction, an “average” dark correction, a “diced” dark correction, an “even-odd” dark correction, and an “N-sheet” dark correction.
The “forward” dark correction uses a “forward” dark image to correct an X-ray image. The “backward” dark correction uses a “backward” dark image. The “average” dark correction uses an “average” dark image. The “diced” dark correction uses a “diced” dark image. The “even-odd” dark correction uses an “even-odd” dark image. The “N-sheet” dark correction uses an “N-sheet” dark image.
FIGS. 13A and 13B illustrate general dark correction methods. FIG. 13A illustrates examples of X-ray images (i.e., I0 to I2, . . . ) and dark images (i.e., D0 to D2, . . . ). FIG. 13B illustrates examples of dark images (i.e., DK0 to DK4, . . . ).
FIG. 13A illustrates a pattern of the dark images and X-ray images alternately acquired in an X-ray fluoroscopic imaging operation. FIG. 13B illustrates a pattern of the dark images continuously captured. Each dark correction method is described below in a case where the dark correction processing is performed on an X-ray image I0 illustrated in FIG. 13A.
The “forward” dark correction is a dark correction that may be performed based on a forward dark image D0 acquired immediately before the X-ray image I0 (i.e., a processing object). The “backward” dark correction is a dark correction that may be performed based on a backward dark image D1 acquired immediately after the X-ray image I0 (i.e., the processing object). The “average” dark correction is a dark correction that may be performed based on an average dark image that can be obtained by averaging the forward dark image D0 and the backward dark image D1 in each pixel.
The “diced” dark correction is a dark correction that may be performed based on a diced dark image that can be generated by alternately selecting the forward dark image D0 and the backward dark image D1. In this case, the selection method for the diced dark image is determined so as to form a diced pattern. The “even-odd” dark correction is a dark correction that may be performed based on an even-odd dark image that can be generated by alternately selecting the forward dark image D0 and the backward dark image D1.
More specifically, the even-odd dark image can be obtained by sequentially selecting the forward dark image D0 or the backward dark image D1 for each line. The “N-sheet” dark correction is a dark correction that may be performed based on an N-sheet dark image that can be generated by averaging N sheets of dark images DK0 to DKN illustrated in FIG. 13B.
FIG. 13C illustrates example dark corrections that can be performed on the X-ray image I0 and the X-ray image I1 illustrated in FIG. 13A according to the above-described various dark correction methods. The following is an example that actually identifies a random noise included in an X-ray image.
The correction includes a first step of removing a dark image from an X-ray image to generate a dark correction-completed X-ray image. The correction includes a second step of calculating a random quantum noise amount σq(X) based on the dark correction-completed X-ray image. The random quantum noise amount σq(X) is dependent on the X-ray intensity “X.” Therefore, the correction includes a step of calculating an average pixel value X of the dark correction-completed X-ray image and calculating the random quantum noise amount σq(X) by inputting the calculated average pixel value X into formula (2).
As understood from the straight line 1202 illustrated in FIG. 12, the random system noise amount σs is a constant value that is independent on the X-ray intensity “X.” Therefore, it is difficult to obtain the random system noise amount σs from the dark correction-completed X-ray image. It is useful to calculate the random system noise amount σs from the dark image.
The correction includes performing dark correction processing on dark images (obtaining a difference between two sheets of dark images) and calculating a standard deviation of the image. When the dark images are used, an X-ray random noise amount corresponding to the X-ray intensity of 0 can be calculated. Namely, the random system noise amount σs can be obtained from the standard deviation. In this case, if the dark image includes a random system noise amount σD, the random system noise amount σs is equal to (√2)σD as illustrated in the following formula (3).σS=√{square root over (12+(−1)2)}σD=√{square root over (2)}σD  (3)
An X-ray image capturing apparatus can operate to capture X-ray images in various shooting modes. For example, the shooting modes include a high quality mode for outputting a noiseless image and a high frame rate mode for outputting a high frame rate image. For example, it may be desired to select the high quality mode if a user intends to diagnose details of a subject. The high frame rate mode may be desired to diagnose a subject that can move quickly.
If the high quality mode is selected, the following processing methods are available for the X-ray image capturing apparatus to output noiseless images.
For example, as a method for increasing the ratio of X-ray image signal to the noise, it may be possible to increase the X-ray amount in an imaging operation. As another method, it may be effective to enhance the degree of random noise suppression processing. Further, as another method, it may be useful to perform the “average dark correction.”
In this case, a dark image can be acquired and used for each X-ray image. A variation in the dark image can be reflected. Accordingly, the accuracy of the dark correction can be increased. If the dark correction is inappropriate, the image quality of an X-ray image may deteriorate due to insufficient removal of dark components. Therefore, it is important to select an optimum dark correction method.
On the other hand, if the high frame rate mode is selected, the following processing methods are available for the X-ray image capturing apparatus to output higher frame rate images.
For example, there is a method for decreasing the X-ray image signal (i.e., X-ray image data) that may be generated by an X-ray sensor. The time required for the image processing can be reduced by decreasing the size of data that may be used for the image processing. As another method, it may be useful to select the image processing that is short in processing time.
Additionally, as another method, it may be useful to select the “N-sheet” dark correction to perform dark correction processing. The time required for acquiring dark images can be reduced by preparing dark images beforehand. However, even if the image processing is speedily performed, the processing itself cannot start unless the next frame is input. In this respect, selecting an optimum dark correction is important.
As described above, if the dark correction method is changed by selecting an appropriate mode, it may be possible to attain a higher frame rate. However, an output image resulting from random noise suppression processing in a post stage may cause an artifact that deteriorates the image quality.
Moreover, in the high quality mode, the image quality can be maintained at a higher level before executing the random noise suppression processing. However, the artifact may occur in an output image after the random noise suppression processing is started and high quality images may not be obtained. In particular, if the processing object is a moving image, an output image may include a blur caused by the movement.
In short, the above-described conventional technique cannot perform optimum noise suppression processing on X-ray images (i.e., radiographic images) and cannot obtain high quality X-ray images (i.e., high quality radiographic images).
To solve the above-described problems, exemplary embodiments of the present invention are directed to a technique capable of performing optimum noise suppression processing on a radiographic image so that a high quality radiographic image can be acquired.